Report Assessment Ch2 Technology Visions

Data Management Report

Author
Affiliation

Rainer M. Krug

Doi
Abstract

A short description what this is about. This is not a tracditional abstract, but rather something else …

Working Title

IPBES_TCA_Ch2_technology

Code repo

Github - private

Build No: 19

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Introduction

The following steps will be done in documented in this report:

Step 1: Determination of numbers

The search terms are based on the shared google doc. They are cleaned up for the usage in OpenAlex.

Vision

The search terms is vision

Show the code
#|

vision_count <- openalexR::oa_fetch(
    title_and_abstract.search = compact(vision_st),
    count_only = TRUE,
    verbose = TRUE
)[, "count"]

Technology

The search terms is technology

Show the code
#|

technology_count <- openalexR::oa_fetch(
    title_and_abstract.search = compact(technology_st),
    count_only = TRUE,
    verbose = TRUE
)[, "count"]

Vision AND technology

The search term is vision AND technology

Count

Show the code
#|

vision_technology_count <-
    openalexR::oa_fetch(
        title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
        count_only = TRUE,
        verbose = TRUE
    )[, "count"]

Count Subfields

Show the code
#|

vision_technology_subfields <- openalexR::oa_query(
    title_and_abstract.search = compact(paste0("(", vision_st, ") AND (", technology_st, ")")),
    group_by = "primary_topic.subfield.id",
    verbose = TRUE
) |>
    openalexR::oa_request() |>
    dplyr::bind_rows() |>
    dplyr::arrange(key)
Show the code
## clean up missing or wrong vision_technology_subfields$key_display_name
need_cleaning <- is.na(vision_technology_subfields$key_display_name) |
    !is.na(as.numeric(vision_technology_subfields$key_display_name))
Warning: NAs introduced by coercion
Show the code
fine <- !need_cleaning

vision_technology_subfields <- vision_technology_subfields |>
    dplyr::filter(fine) |>
    dplyr::select(key, key_display_name) |>
    dplyr::distinct() |>
    merge(y = vision_technology_subfields[need_cleaning, -2], by = "key") |>
    dplyr::bind_rows(vision_technology_subfields[fine, ]) |>
    dplyr::group_by(key, key_display_name) |>
    dplyr::summarize(count = sum(count))

Results

Number of hits

  • vision:: 105,125,052 hits
  • technology: 14,665,760 hits
  • vision AND technology: 11,793,910 hits

Subfields

The subfields are based on the main topic assigned to each work. There are other topics also assigned, but this one has been identified as the main topic by an algorythm. count is the number of works in the vision AND technology corpus which have been assigned to the subfield.

Please take a look at these subfields of the topics to identify the ones to be filtered out.

The easies would be to download the Excel file through the button and to mark the subfields to be filtered out.

Show the code
IPBES.R::table_dt(vision_technology_subfields, fixedColumns = NULL, fn = "Vision Technology Subfields")

Reuse

Citation

BibTeX citation:
@report{krug,
  author = {Krug, Rainer M.},
  title = {Report {Assessment} {Ch2} {Technology} {Visions}},
  doi = {XXXXXX},
  langid = {en},
  abstract = {A short description what this is about. This is not a
    tracditional abstract, but rather something else ...}
}
For attribution, please cite this work as:
Krug, Rainer M. n.d. “Report Assessment Ch2 Technology Visions.” IPBES Data Management Report. https://doi.org/XXXXXX.